Intelligent Automation

Kognitos reads from and writes to Snowflake, Databricks, BigQuery, and Redshift through native connectors with credential rotation via your existing secrets manager. ML models registered in MLflow, Vertex, or SageMaker can be invoked as inputs to plain-English rules — the symbolic executor still owns the final decision, so model outputs are treated as features, not as actions. The result: intelligent automation that consumes the AI/ML investments your data team has already made, without those models silently deciding money-bearing actions.

Long-running processes are first-class. A single Kognitos automation can span days, suspend for human input, resume on schedule or event, persist state across pauses, and coordinate sub-workflows in parallel. Month-end close runs as a single agentic workflow that orchestrates trial balance preparation, intercompany reconciliation, journal entry posting, and reporting — pausing for sign-offs and exception conversations at the points your control narrative requires. End-to-end claims processing follows the same shape across intake, eligibility, adjudication, and payment.

SOC 2 Type II, HIPAA attestation, signed BAAs, regional data residency in North America, EMEA, and APAC, tenant isolation, and a hard training boundary preventing customer data from training upstream foundation models. Identity integrates with Azure AD, Entra, Okta, Google Workspace via SSO and SCIM. Every transaction emits an immutable plain-English execution log accepted as SOX 404 evidence by Big 4 firms. RBAC limits who can edit which rule sets and approve which promotions. Controls are runtime-native, not bolted on.

Yes — this is the operating-model change. SI engagements typically stay engaged because RPA and traditional intelligent-automation platforms require ongoing developer effort to maintain selectors, retrain models, and patch exception handlers. Kognitos automations are written in plain English by the process owner; rules are versioned and promoted through your existing approvals; conversational exception handling routes to the business owner. Implementation partners help with initial design and integration, but the run state lives with your operations team, not with the SI.

The combination of AI, machine learning, and automation technologies to handle complex, judgment-intensive tasks — including unstructured data processing, exception handling, and adaptive decision-making — that traditional rule-based automation cannot.

Traditional automation handles structured, predictable workflows. Intelligent Automation extends this to tasks that require understanding: reading an email and extracting the right invoice number from free-form text, deciding whether a payment discrepancy is within tolerance or requires escalation, routing a support ticket based on sentiment and urgency. The intelligence can come from multiple AI components: NLP for language understanding, ML for pattern recognition, a rules engine for policy enforcement, and increasingly, LLMs for broader language comprehension. Kognitos represents the most a